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Modelling the Relationships Between Ground and Buildings Using 3D Architectural Topological Models Utilising Graph Machine Learning

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Part of the Digital Innovations in Architecture, Engineering and Construction book series (DIAEC)


Historically, architects have established different approaches to constructing their buildings on the ground. Classifying the building/ground relationship enables the architect to make informed design decisions during the early design stages. Manual handling of this task is time-consuming, complex as well as prone to errors. This paper leveraged Machine Learning (ML) methods to overcome this difficulty by applying Graph Machine Learning (GML) to 3D topological models, to classify the building and ground relationship. The paper workflow comprised two stages. The first stage involved generating 3D synthetic architectural precedents and created a dataset of their dual graph using Topologic, which is software that computes the spatial relationships between elements. The second stage ran the Deep Graph Convolutional Neural Network (DGCNN) using PyTorch, which is a Python machine learning library developed by Facebook. The paper’s results demonstrate that the system effectively classifies the relationship between building and ground, with the ability to predict a new previously unseen architectural building/ground relationship with high accuracy measurement that aligns with DGCNNs benchmark graphs. The paper concludes by reflecting on the advantages and disadvantages of generating a sizeable synthetic dataset with embedded semantic topological graphs as a formal design method, in addition to outlining future work.


  • Graph machine learning
  • 3D graphs topological model
  • Generative large data
  • Architectural topology model
  • Automation building/ground relationship
  • Prediction machine learning

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  • Aish, R., Jabi, W., Lannon, S., Wardhana, N., & Chatzivasileiadi, A. (2018). Topologic: Tools to explore architectural topology. AAG 2018: Advances in Architectural Geometry 2018, 316–341.

    Google Scholar 

  • Alymani, A., Jabi, W., & Corcoran, P. (2022). Graph machine learning classification using architectural 3D topological models. Simulation: Transactions Ofthe Society for Modeling and Simulation International.

  • Berlanda, T. (2014). Architectural topographies: A graphic lexicon of how buildings touch the ground.

    CrossRef  Google Scholar 

  • Chatzivasileiadi, A., Lannon, S., Jabi, W., Wardhana, N. M., & Aish, R. (2018a). Addressing pathways to energy modelling through non-manifold topology. Simulation Series, 50(7), 31–38.

  • Chatzivasileiadi, A., Wardhana, N., Jabi, W., Aish, R., & Lannon, S. (2018b). A review of 3D solid modeling software libraries for non-manifold modeling. Proceedings of CAD’18, 59–65.

  • Eisenstadt, V., Arora, H., Ziegler, C., Bielski, J., Langenhan, C., Althoff, K. D., & Dengel, A. (2021). Exploring optimal ways to represent topological and spatial features of building designs in deep learning methods and applications for architecture. In Projections—Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021 (vol. 1, no. D1, pp. 191–200).

    Google Scholar 

  • Jabi, W. (2015). The potential of non-manifold topology in the early design stages. In ACADIA 2015—Computational Ecologies: Design in the Anthropocene: Proceedings of the 35th Annual Conference of the Association for Computer Aided Design in Architecture (vol. 2015-Octob).

  • Jabi, W. (2016). Linking design and simulation using non-manifold topology. Architectural Science Review, 59(4), 323–334.

  • Jabi, W., Aish, R., Lannon, S., Chatzivasileiadi, A., & Wardhana, N. M. (2018). Topologic A toolkit for spatial and topological modelling. In SHAPE, FORM & GEOMETRY.

  • Jabi, W., & Alymani, A. (2020). Graph machine learning using 3d topological models. SimAUD, 427–434.

    Google Scholar 

  • Jabi, W., Soe, S., Theobald, P., Aish, R., & Lannon, S. (2017). Enhancing parametric design through non-manifold topology. Design Studies, 52, 96–114.

  • Kasaei, H. (2019). OrthographicNet: A deep learning approach for 3D object recognition in open-ended domains.

  • Kriege, N., & Mutzel, P. (2012). Subgraph matching kernels for attributed graphs. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012 (vol. 2, pp. 1015–1022).

    Google Scholar 

  • Lu, Y., Tian, R., Li, A., Wang, X., & del Castillo Lopez Jose Luis, G. (2021). CUBIGRAPH5K: Organizational graph generation for structured architectural floor plan dataset. In Projections—Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021 (vol. 1, pp. 81–90).

    Google Scholar 

  • Orsini, F., Frasconi, P., & de Raedt, L. (2015). Graph invariant kernels. IJCAI International Joint Conference on Artificial Intelligence, 2015-Janua(Ijcai), 3756–3762.

    Google Scholar 

  • Porter, Z. T. (2015). Contested Terrain, 1–6.

    Google Scholar 

  • Porter, Z. T. (2017). Architecture ≠ Landscape: The case against hybridization.

  • Sarkar, K., Varanasi, K., & Stricker, D. (2017). Trained 3d models for CNN based object recognition. In VISIGRAPP 2017—Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (vol. 5, pp. 130–137).

  • Stiny, G. N. (1980). Introduction to shape grammars. ACM SIGGRAPH 2008 Classes, 7(November), 36.

  • Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11, 1201–1242.

  • Vittorio, G. (1982). No Title. Address to the Architectural League.

    Google Scholar 

  • Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2019). A Comprehensive Survey on Graph Neural Networks. XX(Xx), 1–22.

    Google Scholar 

  • Ying, R., Morris, C., Hamilton, W. L., You, J., Ren, X., & Leskovec, J. (2018). Hierarchical graph representation learning with differentiable pooling. Advances in Neural Information Processing Systems, 2018-Decem, 4800–4810.

    Google Scholar 

  • Zhang, M., Cui, Z., Neumann, M., & Chen, Y. (2018). An end-to-end deep learning architecture for graph classification. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 4438–4445).

    Google Scholar 

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Correspondence to Wassim Jabi .

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Alymani, A., Jabi, W., Corcoran, P. (2023). Modelling the Relationships Between Ground and Buildings Using 3D Architectural Topological Models Utilising Graph Machine Learning. In: Mora, P.L., Viana, D.L., Morais, F., Vieira Vaz, J. (eds) Formal Methods in Architecture. FMA 2022. Digital Innovations in Architecture, Engineering and Construction. Springer, Singapore.

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  • Print ISBN: 978-981-99-2216-1

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